In many technical problems for controlling industrial plants, or for the quality control thereof, the basic object consists of inspecting the surface of an object, establishing a dimensional variable and comparing this to a reference variable. Primary measurement variables such as position, length, width or height serve for establishing secondary variables such as area, volume, dimensional accuracy, completeness or the like.
Solving these metrological objects in a quick and contactless fashion, i.e. without wear-and-tear, is a feature of special technology, which records images from the scene of a measurement object and evaluates said images using methods and algorithms from so-called image data processing. These technologies are subsumed under the heading industrial image processing (IIP) and have a broad spectrum of technical applications. Thus, for example, image data processing also has applications in medicine, agriculture or security technology.
Optimizing industrial production methods is a specific challenge from the totality of all applications and, as a result, offers the reduction of rejects or the primarily utilized raw products, and also the reduction in secondary factors such as energy use and ecological harm. By using IIP, an increase in quality and productivity of production plants is pursued, which is reflected in an increase in the efficiency, international competitiveness and securing of jobs.
In order to secure product quality and plant availability, producers of tape- or web materials use inspection systems that typically consist of a multiplicity of cameras, with one respective image sensor. Solutions for maximizing the information obtained from image scenes using methods of multi-sensorial data fusion are also sought after in numerous research establishments active in the research field of technical image processing. Here, the data from a plurality of sensors is combined. Compared to processing individual image scenes, the fused image information allow further conclusions to be drawn in respect of physical events, activities or situations.
The limitations to the use of industrial image processing become apparent in the field of belt inspection systems. Particularly in the case of small production plants with few high-quality goods, the high investment requirements of an IIP system do not always make economic sense because the components used therein are very cost intensive. In addition to this, there is small installation space in small production machines or lack of qualified staff, which prevents the integration of the systems.
Industrial image-processing systems have a signal chain that essentially consists of illumination, imaging optical system, image recorder, data interface and evaluation computer in a single or multiple arrangement. The systems differ according to their type and number in the light sources, the objectives, the sensor technology, the data interface and the evaluation computer. By combining these components in conjunction with appropriate algorithms, it is possible to solve many problems. However, if the measurement should be conducted on a large measurement field, for example, a few hundred centimeters, and, at the same time, at a small distance from the measurement plane or object plane, then complex and comparatively expensive IIP systems are required. Until now, there has been no IIP system, not even as a result of any combination and number of the components listed above, commercially available that constitutes an economically justifiable solution for recording and evaluating large image scenes without distortion or parallax in the case of small installation space, high scene resolution and, at the same time, a high image repetition rate (i.e., measurement rate). However, these features are required simultaneously within many technical applications.
FIG. 1 shows a typical arrangement of an elementary IIP system. Here, a camera 100 is connected to a PC 140. The camera 100 has a field of view 110, also referred to as capture region below, within which an object to be inspected 120, more particularly a component, is arranged, the surface of which forming the object plane 130 to be recorded. The camera records an image scene in which the component 120 is inspected in respect of completeness and/or dimensional accuracy. To this end, the field of view 110, which completely captures the object to be inspected 120, is set in the object plane 130 by selecting a suitable optical imaging system, i.e., of the camera objective. If the measurement object occurs within a machine, the relatively large distance of the camera 100 from the object plane 130 has an effect as a technical barrier.
In order to reduce the object distance, it would be possible to select an objective with a smaller focal length; however, this would have a disadvantageous effect on the imaging performance of the objective. However, the dependence of the imaging factor β′ on the object distance, which is now stronger, is of greater importance here for metrological objects. One possible solution lies in the use of objectives with an object-side telecentric beam path. In order to avoid perspective errors, use can be made of telecentric optical systems (TCOs). These are virtually free of perspectives within the telecentric range. By means of a TCO, objects, irrespective of the object plane thereof, are always recorded in the same reproduction scale. However, the image field of a TC optical system is naturally always smaller than the diameter of the exit lens of the objective and hence it is restricted to a few centimeters. A TC objective with a diameter of 15 cm, however, requires extensive installation space and intensive light sources, and is expensive. IIP systems operating on telecentric principles are hence currently used to measure small parts.
An alternative to this lies in subdividing the measurement region to a plurality of cameras; however, this quickly reaches the limitations of modern frame grabbers and also PCs in the case of a large number of cameras, and moreover creates significant costs. Here, a plurality of conventional cameras are arranged in rows or in the form of a matrix, and are connected to a PC via a conventional frame grabber. However, if this technology were wanted to be used, for example, to construct a matrix of 32×32 cameras, the problem would arise that the 1024 signal lines would not be contacted to any conventional evaluation computer or PC. Conventional PCs are able to incorporate up to four PCI expansion cards or frame grabbers. Even if each frame grabber in turn had four signal channels, it would only be possible to connect 16 cameras to each PC. The restricted computational power of modern PCs and the fact that 64 PCs would be required illustrates this unsuitable system arrangement here. In addition to the high costs overall for the cameras and frame grabbers, the costs of PC plug-in modules and 19″ cabinets for the housing, and the installation space for the switchgear cabinet, the difficult maintainability constitutes a further defect in respect of an integration into industrial surroundings.
In applications where there are high demands on the scene resolution and only little installation space is available, the camera or image sensor is often these days displaced in a translational manner to the relevant image region (region of interest, ROI) by means of a motor-driven xy-drive. This makes it possible to obtain very high resolutions of the scene with high imaging performance. By using appropriate path measurement systems (glass scales) of the translational drives, it is also possible to achieve low measurement uncertainty. Nevertheless, this system constellation also has significant disadvantages. Recording or scanning the scene requires a lot of time since the positioning of the image sensor requires a few 100 ms. Moreover, a mechanical drive is not maintenance-free and free from wear-and-tear, and hence it has a relatively short service life.